# A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks

arXiv: https://arxiv.org/abs/2105.05564 · HTML: https://mlsanity.com/paper/2105.05564/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 168 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 27 of 27)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)
- [Multi-armed Bandits with Application to 5G Small Cells](https://mlsanity.com/paper/1510.00627/) (2015)
- [Temporal Locality in Today's Content Caching: Why it Matters and How to Model it](https://mlsanity.com/paper/1305.7114/) (2013)
- [Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing](https://mlsanity.com/paper/1812.08593/) (2018)
- [Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty](https://mlsanity.com/paper/1904.06258/) (2019)
- [Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching](https://mlsanity.com/paper/2001.03835/) (2020)
- [Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions](https://mlsanity.com/paper/1810.13287/) (2018)
- [A Survey of Deep Learning for Data Caching in Edge Network](https://mlsanity.com/paper/2008.07235/) (2020)
- [Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges](https://mlsanity.com/paper/1612.03184/) (2016)
- [Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks](https://mlsanity.com/paper/2011.08449/) (2020)
- [Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks](https://mlsanity.com/paper/1601.00321/) (2016)
- [Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach](https://mlsanity.com/paper/1906.08812/) (2019)
- [Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks](https://mlsanity.com/paper/1902.10574/) (2019)
- [Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks](https://mlsanity.com/paper/2008.05168/) (2020)
- [A Deep Reinforcement Learning Approach for Dynamic Contents Caching in HetNets](https://mlsanity.com/paper/2004.07911/) (2020)
- [Wireless Powered Mobile Edge Computing: Offloading Or Local Computation?](https://mlsanity.com/paper/2008.07500/) (2020)
- [Probabilistic Caching in Wireless D2D Networks: Cache Hit Optimal vs. Throughput Optimal](https://mlsanity.com/paper/1611.03016/) (2016)
- [Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks](https://mlsanity.com/paper/1903.07364/) (2019)
- [Non-Orthogonal Multiple Access with Wireless Caching for 5G-Enabled Vehicular Networks](https://mlsanity.com/paper/2001.06959/) (2020)

## Abstract (arXiv)

Mobile networks are experiencing tremendous increase in data volume and user density. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting the caches of edge network nodes, such as fixed or mobile access points and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers a viable way for network optimization as opposed to traditional optimization approaches which incur high complexity, or fail to provide optimal solutions. Among the various machine learning categories, reinforcement learning operates in an online and autonomous manner without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching is presented, aiming at highlighting the achieved network gains over conventional caching approaches. Taking into account the heterogeneity of sixth generation (6G) networks in various wireless settings, such as fixed, vehicular and flying networks, learning-aided edge caching is presented, departing from traditional architectures. Furthermore, a categorization according to the desirable performance metric, such as spectral, energy and caching efficiency, average delay, and backhaul and fronthaul offloading is provided. Finally, several open issues are discussed, targeting to stimulate further interest in this important research field.